Project/Library: Machine Learning

[taurenshaman]

这里不仅仅是 list ，还有可能包括 language 和 License ^_^
源： http://lore.chuci.info/taurenshaman/json/4e4438b30fdb4bb59d3481714de0f194
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囧，没有对 JSON 格式化显示，内容长度也不能超过 20000 个字符…… so ，完整的内容以及更好效果还是看看原链接吧
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{
"title": "Project/Library: Mearchine Learning",
"tags": "Mearchine Learning; deep learning; sdk/lib",
"items": [
{
"title": "基于.Net 的机器学习与信号处理框架 Accord Framework/AForge.net",
"description": "Accord 是 AForge.net 的扩展，是一个基于.Net 的机器学习与信号处理框架。它包括了一系列的对图像和音频的机器学习算法，如人脸检测、 SIFT 拼接等等。同时， Accord 支持移动对象的实时跟踪等功能。它提供了一个从神经网络到决策树系统的机器学习库。",
"tags": "机器学习; 信号处理; 框架",
"language": "C#",
"license": "GNU LESSER GENERAL PUBLIC LICENSE Version 2.1",
"url": "https://github.com/accord-net/framework/",
"reference": [
"http://accord-framework.net",
"http://www.aforgenet.com"
]
},
{
"title": "来自 Airbnb 的开源机器学习软件包 Aerosolve",
"description": "A machine learning package built for humans.",
"tags": "机器学习; Airbnb",
"language": "Java; Scala",
"license": "Apache License Version 2.0",
"url": "https://github.com/airbnb/aerosolve",
"reference": [
"http://www.infoq.com/cn/news/2015/06/airbnb-release-aerosolve"
]
},
{
"title": "Amazon 机器学习 API",
"description": "Amazon 机器学习 API 让用户不需要大量的数据专家就能够实现模型构建、数据清洗和统计分析等工作，简化了预测的实现流程。虽然该 API 有一些 UI 界面或者算法上的限制，但是却是用户友好和向导驱动的，它为开发者提供了一些可视化工具，让相关 API 的使用更直观、也更清晰。 Amazon 机器学习 API 支持的用户场景包括： 1 、通过分析信号水平特征对歌曲进行题材分类。 2 、通过对智能设备加速传感器捕获的数据以及陀螺仪的信号进行分析识别用户的活动，是上楼、下楼、平躺、坐下还是站立不动。 3 、通过分析用户行为预测用户是否能够成为付费用户。 4 、分析网站活动记录，发现系统中的假用户、机器人以及垃圾邮件制造者。",
"tags": "Mearchine Learning API",
"language": null,
"license": null,
"url": "https://aws.amazon.com/cn/machine-learning/",
"reference": null
},
{
"title": "BigML",
"description": "BigML 是一个对用户友好、对开发者友好的机器学习 API ，该项目的动机是让预测分析对用户而言更简单也更容易理解。 BigML API 提供了 3 种重要的模式：命令行接口、 Web 接口和 RESTful API ，其支持的主要功能包括异常检测、聚类分析、决策树的 SunBurst 可视化以及文本分析等。借助于 BigML ，用户能够通过创建一个描述性的模型来理解复杂数据中各个属性和预测属性之间的关系，能够根据过去的样本数据创建预测模型，能够在 BigML 平台上维护模型并在远程使用。",
"tags": "Mearchine Learning API",
"language": null,
"license": null,
"url": "https://bigml.com/",
"reference": null
},
{
"title": "Blocks",
"description": "Blocks is a framework that helps you build neural network models on top of Theano.",
"tags": "Theano",
"language": "Python",
"license": "MIT",
"url": "https://github.com/mila-udem/blocks",
"reference": null
},
{
"title": "Caffe",
"description": "Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.",
"tags": "",
"language": "C++; Python; CUDA",
"license": "BSD 2-Clause license",
"url": "https://github.com/BVLC/caffe",
"reference": [
"http://caffe.berkeleyvision.org"
]
},
{
"title": "Cassovary",
"description": "Cassovary is a simple \"big graph\" processing library for the JVM. Most JVM-hosted graph libraries are flexible but not space efficient. Cassovary is designed from the ground up to first be able to efficiently handle graphs with billions of nodes and edges. A typical example usage is to do large scale graph mining and analysis of a big network. Cassovary is written in Scala and can be used with any JVM-hosted language. It comes with some common data structures and algorithms.",
"tags": "JVM; big graph processing",
"language": "Scala",
"license": "Apache License Version 2.0",
"url": "https://github.com/twitter/cassovary",
"reference": [
"http://twitter.com/cassovary"
]
},
{
"title": "Chainer",
"description": "A Powerful, Flexible, and Intuitive Framework of Neural Networks. 深度学习的神经网络灵活框架。 Chainer 支持各种网络架构，包括 Feed-forward Nets 、 Convnets 、 Recurrent Nets 和 Recursive Nets 。它也支持 per-batch 的架构。 Chainer 支持 CUDA 计算，它在驱动 GPU 时只需要几行代码。它也能通过一些努力，运行在多 GPUs 的架构中。",
"tags": "",
"language": "Python",
"license": "MIT",
"url": "https://github.com/pfnet/chainer",
"reference": [
"http://chainer.org"
]
},
{
"title": "CNTK: Computational Network Toolkit",
"description": "CNTK, the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.",
"tags": "CNTK; DNN; CNN; RNN; LSTM; SGD",
"language": "C++",
"license": null,
"url": "https://github.com/Microsoft/CNTK/",
"reference": [
"http://research.microsoft.com/en-us/um/people/dongyu/CNTK-Tutorial-NIPS2015.pdf",
"http://blogs.microsoft.com/next/2015/12/10/microsoft-researchers-win-imagenet-computer-vision-challenge/"
]
},
{
"title": "ConvNet",
"description": "Convolutional Neural Networks for Matlab, including Invariang Backpropagation algorithm (IBP). Has versions for GPU and CPU, written on CUDA, C++ and Matlab. All versions work identically. The GPU version uses kernels from Alex Krizhevsky's library 'cuda-convnet2'.",
"tags": "IBP",
"language": "C++; CUDA; Matlab",
"license": null,
"url": "https://github.com/sdemyanov/ConvNet",
"reference": [
"http://www.demyanov.net/"
]
},
{
"title": "基于 JavaScript 的在线深度学习库 ConvNetJS",
"description": "ConvNetJS 是一款基于 JavaScript 的在线深度学习库，它提供了在线的深度学习训练方式。它能够帮助深度学习的初学者更快、更加直观的理解算法，通过一些简单的 Demo 给用户最直观的解释。",
"tags": "Deep Learning",
"language": "JavaScript",
"license": "MIT",
"url": "https://github.com/karpathy/convnetjs",
"reference": [
"http://cs.stanford.edu/people/karpathy/convnetjs/"
]
},
{
"title": "基于 GPU 加速的神经网络应用程序机器学习库 CUDA-Convnet",
"description": "CUDA 是我们众所周知的 GPU 加速套件。而 CUDA-Convnet 是一个基于 GPU 加速的神经网络应用程序机器学习库。它使用 C++编写，并且使用了 NVidia 的 CUDA GPU 处理技术。目前，这个项目已经被重组成为 CUDA-Convnet2 ，支持多个 GPU 和 Kepler-generation GPUs. Vuples 项目与之类似，使用 F#语言编写，并且适用于.Net 平台上。",
"tags": "机器学习; 人工神经网络",
"language": "CUDA; C++; F#",
"license": null,
"url": "https://code.google.com/p/CUDA-convnet2/",
"reference": null
},
{
"title": "darch",
"description": "Create deep architectures in the R programming language. darch package can be used for generating neural networks with many layers (deep architectures). Training methods includes a pre training with the contrastive divergence method and a fine tuning with common known training algorithms like backpropagation or conjugate gradient.",
"tags": "",
"language": "R; C++",
"license": "GPLv3 (GNU GENERAL PUBLIC LICENSE Version 3)",
"url": "https://github.com/maddin79/darch",
"reference": [
"http://cran.um.ac.ir/web/packages/darch/index.html"
]
},
{
"title": "Datumbox",
"description": "Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.",
"tags": "Machine Learning",
"language": "Java",
"license": "Apache License Version 2.0",
"url": "https://github.com/datumbox/datumbox-framework",
"reference": [
"http://www.datumbox.com"
]
},
{
"title": "DeepLearning",
"description": "Code to build MLP models for outdoor head orientation tracking",
"tags": "",
"language": "C++; Python",
"license": null,
"url": "https://github.com/vishwa-raman/DeepLearning",
"reference": null
},
{
"title": "DeepLearnToolbox",
"description": "Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started. NO LONGER MAINTAINED.",
"tags": "",
"language": "Matlab",
"license": null,
"url": "https://github.com/rasmusbergpalm/DeepLearnToolbox",
"reference": null
},
{
"title": "deepnet",
"description": "deepnet is a GPU-based python implementation of deep learning algorithms like Feed-forward Neural Nets, Restricted Boltzmann Machines, Deep Belief Nets, Autoencoders, Deep Boltzmann Machines and Convolutional Neural Nets.",
"tags": "cudamat; cuda-convnet",
"language": "Python; C++",
"license": null,
"url": "https://github.com/nitishsrivastava/deepnet",
"reference": null
},
{
"title": "deepnet: deep learning toolkit in R",
"description": "Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.",
"tags": "",
"language": null,
"license": "GPLv3 (GNU GENERAL PUBLIC LICENSE Version 3)",
"url": "",
"reference": [
"https://cran.r-project.org/web/packages/deepnet/index.html"
]
},
{
"title": "DeepPy: Deep learning in Python",
"description": "DeepPy is a Pythonic deep learning framework built on top of NumPy (with CUDA acceleration).",
"tags": "",
"language": "Python",
"license": "MIT",
"url": "https://github.com/andersbll/deeppy",
"reference": null
},
{
"title": "NVIDIA DIGITS (The NVIDIA Deep Learning GPU Training System)",
"description": "The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers. Quickly design the best deep neural network (DNN) for your data using real-time network behavior visualization. Best of all, DIGITS is a complete system so you don ’ t have to write any code. Get started with DIGITS in under an hour.",
"tags": "",
"language": null,
"license": null,
"url": "",
"reference": [
"https://developer.nvidia.com/digits"
]
},
{
"title": "DL4J/Deeplearning4j",
"description": "Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments, rather than as a research tool.",
"tags": "deep learning",
"language": "Java; Scala; Clojure",
"license": "Apache License Version 2.0",
"url": "https://github.com/deeplearning4j/deeplearning4j",
"reference": [
"http://deeplearning4j.org/"
]
},
{
"title": "DMKT: Distributed Machine Learning Toolkit",
"description": "DMTK 由一个服务于分布式机器学习的框架和一组分布式机器学习算法构成，是一个将机器学习算法应用在大数据上的强大工具包。微软亚洲研究院通过 Github 将分布式机器学习工具包(DMTK ）开源。",
"tags": "DMKT framework; big data; big model; flexibility; efficiency; multiverso; LightLDA; Distributed word embedding; Distributed skipgram mixture model",
"language": "C++",
"license": "MIT",
"url": "https://github.com/Microsoft/DMTK",
"reference": [
"http://www.dmtk.io"
]
},
{
"title": "DNNGraph - A deep neural network model generation DSL in Haskell",
"description": "A DSL for deep neural networks, supporting Caffe and Torch.",
"tags": "",
"language": "Haskell; Protocol Buffer",
"license": "BSD License",
"url": "https://github.com/ajtulloch/dnngraph",
"reference": null
},
{
"title": "eblearn",
"description": "eblearn is an open-source C++ library of machine learning by New York University ’ s machine learning lab, led by Yann LeCun. In particular, implementations of convolutional neural networks with energy-based models along with a GUI, demos and tutorials.",
"tags": "",
"language": "C++",
"license": null,
"url": "http://eblearn.sourceforge.net/",
"reference": null
},
{
"title": "Encog Machine Learning Framework",
"description": "Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train machine learning algorithms.",
"tags": "Machine Learning",
"language": "C; C#; Java; JavaScript",
"license": "Apache License Version 2.0",
"url": "https://github.com/encog",
"reference": [
"http://www.heatonresearch.com/encog/"
]
}
]
}